ColossalAI/tests/test_pipeline/test_schedule/test_oneF_oneB.py

import copy
from functools import partial
from types import MethodType

import pytest
import torch
import torch.nn as nn

import colossalai
from colossalai.cluster import ProcessGroupMesh
from colossalai.interface import OptimizerWrapper
from colossalai.pipeline.schedule.one_f_one_b import OneForwardOneBackwardSchedule
from colossalai.pipeline.stage_manager import PipelineStageManager
from colossalai.testing import rerun_if_address_is_in_use, spawn
from colossalai.testing.random import seed_all


class MlpModel(nn.Module):
    def __init__(self):
        super(MlpModel, self).__init__()
        self.linear1 = nn.Linear(4, 8)
        self.linear2 = nn.Linear(8, 4)

    def forward(self, x):
        x = self.linear1(x)
        x = self.linear2(x)
        return x


def pp_linear_fwd(
    forward, data: torch.Tensor = None, input_obj: torch.Tensor = None, stage_mgr: PipelineStageManager = None
):
    if stage_mgr.is_first_stage():
        return {"input_obj": forward(data)}
    elif stage_mgr.is_last_stage():
        return forward(input_obj)
    else:
        return {"input_obj": forward(input_obj)}


def examine_pp():
    """
    This test is to examine the correctness of 1F1B, compared with torch.
    Be aware it contains some hardcodes.
    """
    world_size = torch.distributed.get_world_size()
    local_rank = torch.distributed.get_rank()
    seed_all(1453)

    NUM_MICRO_BATCHS = 4
    BATCH_SIZE = 4

    # create models
    torch_model = MlpModel().cuda()

    pp_model = copy.deepcopy(torch_model).cuda()

    DP_DIM, PP_DIM, TP_DIM = 0, 1, 2
    pg_mesh = ProcessGroupMesh(1, world_size, 1)
    stage_manager = PipelineStageManager(pg_mesh, PP_DIM)
    schedule = OneForwardOneBackwardSchedule(stage_manager, num_microbatches=NUM_MICRO_BATCHS)

    for idx, (_, sub_model) in enumerate(pp_model.named_children()):
        if idx % (world_size) == local_rank:
            sharded_model = sub_model.cuda()

    sharded_model._forward = sharded_model.forward
    sharded_model.forward = MethodType(partial(pp_linear_fwd, stage_mgr=stage_manager), sharded_model._forward)

    # create optimizer
    torch_optimizer = torch.optim.SGD(torch_model.parameters(), lr=1)
    pp_optimizer = OptimizerWrapper(torch.optim.SGD(sharded_model.parameters(), lr=1))

    # create
    seed_all(1453)
    if stage_manager.is_first_stage():
        input_list = [torch.rand(BATCH_SIZE, 4).cuda()]
    else:
        input_list = [torch.zeros(BATCH_SIZE, 4).cuda()]
    torch.distributed.all_reduce(input_list[0])

    criterion = lambda x, y: torch.mean(x)

    # forward and backward
    torch_output = torch_model(input_list[0])
    torch_loss = criterion(torch_output, _)
    torch_loss.backward()

    pp_ret = schedule.forward_backward_step(
        sharded_model, iter(input_list), criterion, pp_optimizer, return_loss=True, return_outputs=True
    )

    # check loss
    if stage_manager.is_last_stage():
        assert torch.allclose(torch_loss, pp_ret["loss"])

    # check gradients
    torch_grad = []
    for torch_p in torch_model.parameters():
        torch_grad.append(torch_p.grad.data)
    for idx, pp_p in enumerate(sharded_model.parameters()):
        assert torch.allclose(torch_grad[idx + local_rank * 2], pp_p.grad.data)

    # step
    torch_optimizer.step()
    pp_optimizer.step()

    # check updated param
    torch_param = []
    for torch_p in torch_model.parameters():
        torch_param.append(torch_p.data)
    for idx, pp_p in enumerate(sharded_model.parameters()):
        assert torch.allclose(torch_param[idx + local_rank * 2], pp_p.data)


def run_dist(rank, world_size, port):
    colossalai.launch(config=dict(), rank=rank, world_size=world_size, port=port, host="localhost")
    examine_pp()


@pytest.mark.dist
@rerun_if_address_is_in_use()
def test_pp():
    spawn(run_dist, 2)


if __name__ == "__main__":
    test_pp()